{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Impact of neglecting the self-demagnetization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import BoundaryNorm\n",
    "from matplotlib.cm import get_cmap\n",
    "from fatiando import utils, gridder\n",
    "import mesher, triaxial_ellipsoid\n",
    "from plot_functions import savefig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set some plot parameters\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.dpi'] = 300.\n",
    "rcParams['font.size'] = 6\n",
    "rcParams['xtick.labelsize'] = 'medium'\n",
    "rcParams['ytick.labelsize'] = 'medium'\n",
    "rcParams['axes.labelsize'] = 'large'\n",
    "rcParams['legend.fontsize'] = 'medium'\n",
    "rcParams['savefig.dpi'] = 300."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Warrego magnetite body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# semi-axes (in m)\n",
    "a = 490.7\n",
    "b = 69.7\n",
    "c = 30.\n",
    "\n",
    "# orientation angles (in degrees)\n",
    "strike = -34.\n",
    "dip = 66.1\n",
    "rake = 45."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# isotropic susceptibility (in SI)\n",
    "chi_true = 1.690"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "warrego = mesher.TriaxialEllipsoid(0, 0, 500, a, b, c, strike, dip, rake,\n",
    "                                   props={'principal susceptibilities': [chi_true, chi_true, chi_true],\n",
    "                                          'susceptibility angles': [strike, dip, rake]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# demagnetizing factors\n",
    "n11, n22, n33 = triaxial_ellipsoid.demag_factors(warrego)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local-geomagnetic field (nT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Main coordinate system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "F_xyz = np.array([32610., 0., 39450.])\n",
    "F, inc, dec = utils.vec2ang(F_xyz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51183.1476172 50.4223208627 0.0\n"
     ]
    }
   ],
   "source": [
    "print F, inc, dec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Local coordinate system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "F_local = np.dot(warrego.transf_matrix.T, F_xyz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 49844.03074679 -11610.88695533    688.8417986 ]\n"
     ]
    }
   ],
   "source": [
    "print F_local"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation points (m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "area = [-2000., 2000., -2000., 2000.]\n",
    "shape = (100, 100)\n",
    "xp, yp, zp = gridder.regular(area, shape, z = 0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total-field anomaly (nT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\chi = \\chi_{true}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon = 8.403 percent\n"
     ]
    }
   ],
   "source": [
    "# true magnetization in the main system\n",
    "mag_true = triaxial_ellipsoid.magnetization(warrego, F, inc, dec, demag=True)\n",
    "\n",
    "# approximated magnetization\n",
    "mag_true_approx = triaxial_ellipsoid.magnetization(warrego, F, inc, dec, demag=False)\n",
    "\n",
    "# relative error\n",
    "mag_true_norm = np.linalg.norm(mag_true, ord = 2)\n",
    "mag_true_approx_norm = np.linalg.norm(mag_true_approx, ord = 2)\n",
    "delta_mag_true_norm = np.linalg.norm(mag_true - mag_true_approx, ord = 2)\n",
    "epsilon_true = delta_mag_true_norm/mag_true_norm\n",
    "\n",
    "print 'epsilon = %.3f percent' % (epsilon_true*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 44.36562777  -3.34636713  48.66805892] 65.9400262282\n"
     ]
    }
   ],
   "source": [
    "print mag_true, mag_true_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.38558608e+01  -2.88105245e-15   5.30546982e+01] 68.834130496\n"
     ]
    }
   ],
   "source": [
    "print mag_true_approx, mag_true_approx_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_true = triaxial_ellipsoid.tf(xp, yp, zp, [warrego], F, inc, dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2b97cV9mZmZn5d5I0dtMWgJrba4+bVK5+zxdmZzv9P7c0ffymKs/GpudLO+zX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\nZFiTJEmSpBoyrEmSJElSDf1/g12phUyTQqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xade0be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.15, 7./3))\n",
    "plt.axis('scaled')\n",
    "\n",
    "ranges = np.max(np.abs([np.min(tf_true), np.max(tf_true)]))\n",
    "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n",
    "\n",
    "plt.contourf(0.001*yp.reshape(shape), 0.001*xp.reshape(shape),\n",
    "             tf_true.reshape(shape), levels=levels,\n",
    "             cmap = cmap, norm=norm)\n",
    "plt.ylabel('x (km)')\n",
    "plt.xlabel('y (km)')\n",
    "plt.xlim(0.001*np.min(yp), 0.001*np.max(yp))\n",
    "plt.ylim(0.001*np.min(xp), 0.001*np.max(xp))\n",
    "cbar = plt.colorbar()\n",
    "\n",
    "plt.tight_layout()\n",
    "savefig('f05.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max = 482.486\n",
      "min = -70.649\n",
      "peak-to-peak amplitude = 553.135\n"
     ]
    }
   ],
   "source": [
    "print 'max = %.3f' % (tf_true.max())\n",
    "print 'min = %.3f' % (tf_true.min())\n",
    "print 'peak-to-peak amplitude = %.3f' % (tf_true.max() - tf_true.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_true_approx = triaxial_ellipsoid.tf(xp, yp, zp, [warrego], F, inc, dec,\n",
    "                                       demag = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "residuals_true = tf_true_approx - tf_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "residuals min  = -3.388\n",
      "residuals mean = 0.243\n",
      "residuals max  = 40.446\n"
     ]
    }
   ],
   "source": [
    "print 'residuals min  = %.3f' % (residuals_true.min())\n",
    "print 'residuals mean = %.3f' % (residuals_true.mean())\n",
    "print 'residuals max  = %.3f' % (residuals_true.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "misfit_true = np.sum(residuals_true*residuals_true)/residuals_true.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "misfit = 10.371\n"
     ]
    }
   ],
   "source": [
    "print 'misfit = %.3f' % misfit_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak-to-peak field amplitude = 43.834 absolute\n"
     ]
    }
   ],
   "source": [
    "print 'peak-to-peak field amplitude = %.3f absolute' % \\\n",
    "(residuals_true.max() - residuals_true.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak-to-peak field amplitude = 7.925 percent\n"
     ]
    }
   ],
   "source": [
    "print 'peak-to-peak field amplitude = %.3f percent' % \\\n",
    "(100*(residuals_true.max() - residuals_true.min())/(tf_true.max() - tf_true.min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\chi = 0.1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# isotropic susceptibility commonly used as an upper limit\n",
    "# to neglect the self-demagnetization\n",
    "chi_usual = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "warrego_usual = mesher.TriaxialEllipsoid(0, 0, 500, a, b, c, strike, dip, rake,\n",
    "                                         props={'principal susceptibilities': [chi_usual, chi_usual, chi_usual],\n",
    "                                         'susceptibility angles': [strike, dip, rake]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon = 0.675 percent\n"
     ]
    }
   ],
   "source": [
    "# true magnetization in the main system\n",
    "mag_usual = triaxial_ellipsoid.magnetization(warrego_usual, F, inc, dec,\n",
    "                                            demag=True)\n",
    "\n",
    "# approximated magnetization\n",
    "mag_usual_approx = triaxial_ellipsoid.magnetization(warrego_usual, F, inc, dec,\n",
    "                                                   demag=False)\n",
    "\n",
    "# relative error\n",
    "mag_usual_norm = np.linalg.norm(mag_usual, ord = 2)\n",
    "mag_usual_approx_norm = np.linalg.norm(mag_usual_approx, ord = 2)\n",
    "delta_mag_usual_norm = np.linalg.norm(mag_usual - mag_usual_approx, ord = 2)\n",
    "epsilon_usual = delta_mag_usual_norm/mag_usual_norm\n",
    "\n",
    "print 'epsilon = %.3f percent' % (epsilon_usual*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.59924155 -0.01822971  3.11927956] 4.06033175251\n"
     ]
    }
   ],
   "source": [
    "print mag_usual, mag_usual_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2.59502135e+00  -2.17834865e-16   3.13933125e+00] 4.07302547314\n"
     ]
    }
   ],
   "source": [
    "print mag_usual_approx, mag_usual_approx_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_usual = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_usual], F, inc, dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_usual_approx = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_usual], F, inc, dec,\n",
    "                                        demag = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "residuals_usual = tf_usual_approx - tf_usual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "residuals min  = -0.021\n",
      "residuals mean = 0.001\n",
      "residuals max  = 0.192\n"
     ]
    }
   ],
   "source": [
    "print 'residuals min  = %.3f' % (residuals_usual.min())\n",
    "print 'residuals mean = %.3f' % (residuals_usual.mean())\n",
    "print 'residuals max  = %.3f' % (residuals_usual.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "misfit_usual = np.sum(residuals_usual*residuals_usual)/residuals_usual.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "misfit = 0.000\n"
     ]
    }
   ],
   "source": [
    "print 'misfit = %.3f' % misfit_usual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak-to-peak field amplitude = 0.213 absolute\n"
     ]
    }
   ],
   "source": [
    "print 'peak-to-peak field amplitude = %.3f absolute' % \\\n",
    "(residuals_usual.max() - residuals_usual.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak-to-peak field amplitude = 0.616 percent\n"
     ]
    }
   ],
   "source": [
    "print 'peak-to-peak field amplitude = %.3f percent' % \\\n",
    "(100*(residuals_usual.max() - residuals_usual.min())/(tf_usual.max() - tf_usual.min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\chi = \\chi_{max}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi_max = 0.116 SI\n"
     ]
    }
   ],
   "source": [
    "# proposed upper maximum isotropic susceptibility\n",
    "# to guarantee a relative error lower than or equal to epsilon\n",
    "#epsilon_max = 0.07 # this epsilon generates a misfit similar \n",
    "                   # to that obtained by using the usual chi = 0.1\n",
    "epsilon_max = 0.08\n",
    "chi_max = epsilon_max/n33\n",
    "\n",
    "print 'chi_max = %.3f SI' % chi_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "warrego_max = mesher.TriaxialEllipsoid(0, 0, 500, a, b, c, strike, dip, rake,\n",
    "                                       props={'principal susceptibilities': [chi_max, chi_max, chi_max],\n",
    "                                              'susceptibility angles': [strike, dip, rake]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon = 0.781 percent\n"
     ]
    }
   ],
   "source": [
    "# true magnetization in the main system\n",
    "mag_max = triaxial_ellipsoid.magnetization(warrego_max, F, inc, dec,\n",
    "                                           demag=True)\n",
    "\n",
    "# approximated magnetization\n",
    "mag_max_approx = triaxial_ellipsoid.magnetization(warrego_max, F, inc, dec,\n",
    "                                                  demag=False)\n",
    "\n",
    "# relative error\n",
    "mag_max_norm = np.linalg.norm(mag_max, ord = 2)\n",
    "mag_max_approx_norm = np.linalg.norm(mag_max_approx, ord = 2)\n",
    "delta_mag_max_norm = np.linalg.norm(mag_max - mag_max_approx, ord = 2)\n",
    "\n",
    "epsilon_calculated = delta_mag_max_norm/mag_max_norm\n",
    "\n",
    "print 'epsilon = %.3f percent' % (epsilon_calculated*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3.01645036 -0.02440124  3.61542732] 4.70859670485\n"
     ]
    }
   ],
   "source": [
    "print mag_max, mag_max_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  3.01081106e+00  -2.17834865e-16   3.64233353e+00] 4.72562978042\n"
     ]
    }
   ],
   "source": [
    "print mag_max_approx, mag_max_approx_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_max = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_max], F, inc, dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf_max_approx = triaxial_ellipsoid.tf(xp, yp, zp, [warrego_max], F, inc, dec,\n",
    "                                      demag = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "residuals_max = tf_max_approx - tf_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "residuals min  = -0.028\n",
      "residuals mean = 0.002\n",
      "residuals max  = 0.257\n"
     ]
    }
   ],
   "source": [
    "print 'residuals min  = %.3f' % (residuals_max.min())\n",
    "print 'residuals mean = %.3f' % (residuals_max.mean())\n",
    "print 'residuals max  = %.3f' % (residuals_max.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "misfit_max = np.sum(residuals_max*residuals_max)/residuals_max.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "misfit = 0.000\n"
     ]
    }
   ],
   "source": [
    "print 'misfit = %.3f' % misfit_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak-to-peak field amplitude = 0.285 absolute\n"
     ]
    }
   ],
   "source": [
    "print 'peak-to-peak field amplitude = %.3f absolute' % \\\n",
    "(residuals_max.max() - residuals_max.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak-to-peak field amplitude = 0.712 percent\n"
     ]
    }
   ],
   "source": [
    "print 'peak-to-peak field amplitude = %.3f percent' % \\\n",
    "(100*(residuals_max.max() - residuals_max.min())/(tf_max.max() - tf_max.min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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bkoqIHSJidkTMiojZbPpn1TewrXj1DWt3dkRsLF7zR+j6AuB+ajeNujIiDi7a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\n+VqutVFxijQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16c38d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.15, 7))\n",
    "plt.axis('scaled')\n",
    "\n",
    "ranges = np.max(np.abs([np.min(residuals_true), np.max(residuals_true)]))\n",
    "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n",
    "plt.subplot(3,1,1)\n",
    "plt.contourf(0.001*yp.reshape(shape), 0.001*xp.reshape(shape),\n",
    "             residuals_true.reshape(shape), levels=levels,\n",
    "             cmap = cmap, norm=norm)\n",
    "plt.ylabel('x (km)')\n",
    "plt.xlim(0.001*np.min(yp), 0.001*np.max(yp))\n",
    "plt.ylim(0.001*np.min(xp), 0.001*np.max(xp))\n",
    "cbar = plt.colorbar()\n",
    "plt.annotate(s='(a)', xy=(0.88,0.92),\n",
    "             xycoords = 'axes fraction', color='k',\n",
    "             fontsize = 10)\n",
    "\n",
    "ranges = np.max(np.abs([np.min(residuals_usual), np.max(residuals_usual)]))\n",
    "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.contourf(0.001*yp.reshape(shape), 0.001*xp.reshape(shape),\n",
    "             residuals_usual.reshape(shape), levels=levels,\n",
    "             cmap = cmap, norm=norm)\n",
    "plt.ylabel('x (km)')\n",
    "plt.xlim(0.001*np.min(yp), 0.001*np.max(yp))\n",
    "plt.ylim(0.001*np.min(xp), 0.001*np.max(xp))\n",
    "plt.colorbar()\n",
    "plt.annotate(s='(b)', xy=(0.88,0.92),\n",
    "             xycoords = 'axes fraction', color='k',\n",
    "             fontsize = 10)\n",
    "\n",
    "ranges = np.max(np.abs([np.min(residuals_max), np.max(residuals_max)]))\n",
    "levels = MaxNLocator(nbins=20).tick_values(-ranges, ranges)\n",
    "cmap = plt.get_cmap('RdBu_r')\n",
    "norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "plt.contourf(0.001*yp.reshape(shape), 0.001*xp.reshape(shape),\n",
    "             residuals_max.reshape(shape), levels=levels,\n",
    "             cmap = cmap, norm=norm)\n",
    "plt.ylabel('x (km)')\n",
    "plt.xlabel('y (km)')\n",
    "plt.xlim(0.001*np.min(yp), 0.001*np.max(yp))\n",
    "plt.ylim(0.001*np.min(xp), 0.001*np.max(xp))\n",
    "plt.colorbar()\n",
    "plt.annotate(s='(c)', xy=(0.88,0.92), \n",
    "             xycoords = 'axes fraction', color='k',\n",
    "             fontsize = 10)\n",
    "\n",
    "plt.tight_layout()\n",
    "savefig('f06.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
